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HAVING quals. Normally this is an insignificant effect --- but it will not be insignificant when these clauses contain sub-selects. The added costs cannot affect the planning of the query containing them, but they might have an impact when the query is a sub-query of a larger one. |
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README |
Summary ------- These directories take the Query structure returned by the parser, and generate a plan used by the executor. The /plan directory generates the actual output plan, the /path code generates all possible ways to join the tables, and /prep handles various preprocessing steps for special cases. /util is utility stuff. /geqo is the separate "genetic optimization" planner --- it does a semi-random search through the join tree space, rather than exhaustively considering all possible join trees. (But each join considered by /geqo is given to /path to create paths for, so we consider all possible implementation paths for each specific join pair even in GEQO mode.) Paths and Join Pairs -------------------- During the planning/optimizing process, we build "Path" trees representing the different ways of doing a query. We select the cheapest Path that generates the desired relation and turn it into a Plan to pass to the executor. (There is pretty much a one-to-one correspondence between the Path and Plan trees, but Path nodes omit info that won't be needed during planning, and include info needed for planning that won't be needed by the executor.) The optimizer builds a RelOptInfo structure for each base relation used in the query. Base rels are either primitive tables, or subquery subselects that are planned via a separate recursive invocation of the planner. A RelOptInfo is also built for each join relation that is considered during planning. A join rel is simply a combination of base rels. There is only one join RelOptInfo for any given set of baserels --- for example, the join {A B C} is represented by the same RelOptInfo no matter whether we build it by joining A and B first and then adding C, or joining B and C first and then adding A, etc. These different means of building the joinrel are represented as Paths. For each RelOptInfo we build a list of Paths that represent plausible ways to implement the scan or join of that relation. Once we've considered all the plausible Paths for a rel, we select the one that is cheapest according to the planner's cost estimates. The final plan is derived from the cheapest Path for the RelOptInfo that includes all the base rels of the query. Possible Paths for a primitive table relation include plain old sequential scan, plus index scans for any indexes that exist on the table. A subquery base relation just has one Path, a "SubqueryScan" path (which links to the subplan that was built by a recursive invocation of the planner). Likewise a function-RTE base relation has only one possible Path. Joins always occur using two RelOptInfos. One is outer, the other inner. Outers drive lookups of values in the inner. In a nested loop, lookups of values in the inner occur by scanning the inner path once per outer tuple to find each matching inner row. In a mergejoin, inner and outer rows are ordered, and are accessed in order, so only one scan is required to perform the entire join: both inner and outer paths are scanned in-sync. (There's not a lot of difference between inner and outer in a mergejoin...) In a hashjoin, the inner is scanned first and all its rows are entered in a hashtable, then the outer is scanned and for each row we lookup the join key in the hashtable. A Path for a join relation is actually a tree structure, with the top Path node representing the join method. It has left and right subpaths that represent the scan or join methods used for the two input relations. Join Tree Construction ---------------------- The optimizer generates optimal query plans by doing a more-or-less exhaustive search through the ways of executing the query. The best Path tree is found by a recursive process: 1) Take each base relation in the query, and make a RelOptInfo structure for it. Find each potentially useful way of accessing the relation, including sequential and index scans, and make a Path representing that way. All the Paths made for a given relation are placed in its RelOptInfo.pathlist. (Actually, we discard Paths that are obviously inferior alternatives before they ever get into the pathlist --- what ends up in the pathlist is the cheapest way of generating each potentially useful sort ordering of the relation.) Also create RelOptInfo.joininfo nodes that list all the join clauses that involve this relation. For example, the WHERE clause "tab1.col1 = tab2.col1" generates a JoinInfo for tab1 listing tab2 as an unjoined relation, and also one for tab2 showing tab1 as an unjoined relation. If we have only a single base relation in the query, we are done. Otherwise we have to figure out how to join the base relations into a single join relation. 2) If the query's FROM clause contains explicit JOIN clauses, we join those pairs of relations in exactly the tree structure indicated by the JOIN clauses. (This is absolutely necessary when dealing with outer JOINs. For inner JOINs we have more flexibility in theory, but don't currently exploit it in practice.) For each such join pair, we generate a Path for each feasible join method, and select the cheapest Path. Note that the JOIN clause structure determines the join Path structure, but it doesn't constrain the join implementation method at each join (nestloop, merge, hash), nor does it say which rel is considered outer or inner at each join. We consider all these possibilities in building Paths. 3) At the top level of the FROM clause we will have a list of relations that are either base rels or joinrels constructed per JOIN directives. We can join these rels together in any order the planner sees fit. The standard (non-GEQO) planner does this as follows: Consider joining each RelOptInfo to each other RelOptInfo specified in its RelOptInfo.joininfo, and generate a Path for each possible join method for each such pair. (If we have a RelOptInfo with no join clauses, we have no choice but to generate a clauseless Cartesian-product join; so we consider joining that rel to each other available rel. But in the presence of join clauses we will only consider joins that use available join clauses.) If we only had two relations in the FROM list, we are done: we just pick the cheapest path for the join RelOptInfo. If we had more than two, we now need to consider ways of joining join RelOptInfos to each other to make join RelOptInfos that represent more than two FROM items. The join tree is constructed using a "dynamic programming" algorithm: in the first pass (already described) we consider ways to create join rels representing exactly two FROM items. The second pass considers ways to make join rels that represent exactly three FROM items; the next pass, four items, etc. The last pass considers how to make the final join relation that includes all FROM items --- obviously there can be only one join rel at this top level, whereas there can be more than one join rel at lower levels. At each level we use joins that follow available join clauses, if possible, just as described for the first level. For example: SELECT * FROM tab1, tab2, tab3, tab4 WHERE tab1.col = tab2.col AND tab2.col = tab3.col AND tab3.col = tab4.col Tables 1, 2, 3, and 4 are joined as: {1 2},{2 3},{3 4} {1 2 3},{2 3 4} {1 2 3 4} (other possibilities will be excluded for lack of join clauses) SELECT * FROM tab1, tab2, tab3, tab4 WHERE tab1.col = tab2.col AND tab1.col = tab3.col AND tab1.col = tab4.col Tables 1, 2, 3, and 4 are joined as: {1 2},{1 3},{1 4} {1 2 3},{1 3 4},{1 2 4} {1 2 3 4} We consider left-handed plans (the outer rel of an upper join is a joinrel, but the inner is always a single FROM item); right-handed plans (outer rel is always a single item); and bushy plans (both inner and outer can be joins themselves). For example, when building {1 2 3 4} we consider joining {1 2 3} to {4} (left-handed), {4} to {1 2 3} (right-handed), and {1 2} to {3 4} (bushy), among other choices. Although the jointree scanning code produces these potential join combinations one at a time, all the ways to produce the same set of joined base rels will share the same RelOptInfo, so the paths produced from different join combinations that produce equivalent joinrels will compete in add_path. Once we have built the final join rel, we use either the cheapest path for it or the cheapest path with the desired ordering (if that's cheaper than applying a sort to the cheapest other path). Pulling up subqueries --------------------- As we described above, a subquery appearing in the range table is planned independently and treated as a "black box" during planning of the outer query. This is necessary when the subquery uses features such as aggregates, GROUP, or DISTINCT. But if the subquery is just a simple scan or join, treating the subquery as a black box may produce a poor plan compared to considering it as part of the entire plan search space. Therefore, at the start of the planning process the planner looks for simple subqueries and pulls them up into the main query's jointree. Pulling up a subquery may result in FROM-list joins appearing below the top of the join tree. Each FROM-list is planned using the dynamic-programming search method described above. If pulling up a subquery produces a FROM-list as a direct child of another FROM-list (with no explicit JOIN directives between), then we can merge the two FROM-lists together. Once that's done, the subquery is an absolutely integral part of the outer query and will not constrain the join tree search space at all. However, that could result in unpleasant growth of planning time, since the dynamic-programming search has runtime exponential in the number of FROM-items considered. Therefore, we don't merge FROM-lists if the result would have too many FROM-items in one list. Optimizer Functions ------------------- The primary entry point is planner(). planner() set up for recursive handling of subqueries do final cleanup after planning. -subquery_planner() pull up subqueries from rangetable, if possible simplify constant expressions canonicalize qual Attempt to reduce WHERE clause to either CNF or DNF canonical form. CNF (top-level-AND) is preferred, since the optimizer can then use any of the AND subclauses to filter tuples; but quals that are in or close to DNF form will suffer exponential expansion if we try to force them to CNF. In pathological cases either transform may expand the qual unreasonably; so we may have to leave it un-normalized, thereby reducing the accuracy of selectivity estimates. process sublinks convert Vars of outer query levels into Params --grouping_planner() preprocess target list for non-SELECT queries handle UNION/INTERSECT/EXCEPT, GROUP BY, HAVING, aggregates, ORDER BY, DISTINCT, LIMIT --query_planner() pull out constant quals, which can be used to gate execution of the whole plan (if any are found, we make a top-level Result node to do the gating) make list of base relations used in query split up the qual into restrictions (a=1) and joins (b=c) find qual clauses that enable merge and hash joins ----make_one_rel() set_base_rel_pathlist() find scan and all index paths for each base relation find selectivity of columns used in joins -----make_one_rel_by_joins() jump to geqo if needed else call make_rels_by_joins() for each level of join tree needed make_rels_by_joins(): For each joinrel of the prior level, do make_rels_by_clause_joins() if it has join clauses, or make_rels_by_clauseless_joins() if not. Also generate "bushy plan" joins between joinrels of lower levels. Back at make_one_rel_by_joins(), apply set_cheapest() to extract the cheapest path for each newly constructed joinrel. Loop back if this wasn't the top join level. Back at query_planner: put back any constant quals by adding a Result node Back at grouping_planner: do grouping(GROUP) do aggregates make unique(DISTINCT) make sort(ORDER BY) make limit(LIMIT/OFFSET) Optimizer Data Structures ------------------------- RelOptInfo - a relation or joined relations RestrictInfo - restriction clauses, like "x = 3" JoinInfo - join clauses, including the relids needed for the join Path - every way to generate a RelOptInfo(sequential,index,joins) SeqScan - a plain Path node with pathtype = T_SeqScan IndexPath - index scans TidPath - scan by CTID AppendPath - append multiple subpaths together ResultPath - a Result plan node (used for variable-free tlist or qual) MaterialPath - a Material plan node NestPath - nested-loop joins MergePath - merge joins HashPath - hash joins PathKeys - a data structure representing the ordering of a path The optimizer spends a good deal of its time worrying about the ordering of the tuples returned by a path. The reason this is useful is that by knowing the sort ordering of a path, we may be able to use that path as the left or right input of a mergejoin and avoid an explicit sort step. Nestloops and hash joins don't really care what the order of their inputs is, but mergejoin needs suitably ordered inputs. Therefore, all paths generated during the optimization process are marked with their sort order (to the extent that it is known) for possible use by a higher-level merge. It is also possible to avoid an explicit sort step to implement a user's ORDER BY clause if the final path has the right ordering already, so the sort ordering is of interest even at the top level. query_planner() will look for the cheapest path with a sort order matching the desired order, and grouping_planner() will compare its cost to the cost of using the cheapest-overall path and doing an explicit sort. When we are generating paths for a particular RelOptInfo, we discard a path if it is more expensive than another known path that has the same or better sort order. We will never discard a path that is the only known way to achieve a given sort order (without an explicit sort, that is). In this way, the next level up will have the maximum freedom to build mergejoins without sorting, since it can pick from any of the paths retained for its inputs. PathKeys -------- The PathKeys data structure represents what is known about the sort order of a particular Path. Path.pathkeys is a List of Lists of PathKeyItem nodes that represent the sort order of the result generated by the Path. The n'th sublist represents the n'th sort key of the result. In single/base relation RelOptInfo's, the Paths represent various ways of scanning the relation and the resulting ordering of the tuples. Sequential scan Paths have NIL pathkeys, indicating no known ordering. Index scans have Path.pathkeys that represent the chosen index's ordering, if any. A single-key index would create a pathkey with a single sublist, e.g. ( (tab1.indexkey1/sortop1) ). A multi-key index generates a sublist per key, e.g. ( (tab1.indexkey1/sortop1) (tab1.indexkey2/sortop2) ) which shows major sort by indexkey1 (ordering by sortop1) and minor sort by indexkey2 with sortop2. Note that a multi-pass indexscan (OR clause scan) has NIL pathkeys since we can say nothing about the overall order of its result. Also, an indexscan on an unordered type of index generates NIL pathkeys. However, we can always create a pathkey by doing an explicit sort. The pathkeys for a Sort plan's output just represent the sort key fields and the ordering operators used. Things get more interesting when we consider joins. Suppose we do a mergejoin between A and B using the mergeclause A.X = B.Y. The output of the mergejoin is sorted by X --- but it is also sorted by Y. We represent this fact by listing both keys in a single pathkey sublist: ( (A.X/xsortop B.Y/ysortop) ). This pathkey asserts that the major sort order of the Path can be taken to be *either* A.X or B.Y. They are equal, so they are both primary sort keys. By doing this, we allow future joins to use either var as a pre-sorted key, so upper Mergejoins may be able to avoid having to re-sort the Path. This is why pathkeys is a List of Lists. We keep a sortop associated with each PathKeyItem because cross-data-type mergejoins are possible; for example int4 = int8 is mergejoinable. In this case we need to remember that the left var is ordered by int4lt while the right var is ordered by int8lt. So the different members of each sublist could have different sortops. Note that while the order of the top list is meaningful (primary vs. secondary sort key), the order of each sublist is arbitrary. Each sublist should be regarded as a set of equivalent keys, with no significance to the list order. With a little further thought, it becomes apparent that pathkeys for joins need not only come from mergejoins. For example, if we do a nestloop join between outer relation A and inner relation B, then any pathkeys relevant to A are still valid for the join result: we have not altered the order of the tuples from A. Even more interesting, if there was a mergeclause (more formally, an "equijoin clause") A.X=B.Y, and A.X was a pathkey for the outer relation A, then we can assert that B.Y is a pathkey for the join result; X was ordered before and still is, and the joined values of Y are equal to the joined values of X, so Y must now be ordered too. This is true even though we used neither an explicit sort nor a mergejoin on Y. More generally, whenever we have an equijoin clause A.X = B.Y and a pathkey A.X, we can add B.Y to that pathkey if B is part of the joined relation the pathkey is for, *no matter how we formed the join*. It works as long as the clause has been applied at some point while forming the join relation. (In the current implementation, we always apply qual clauses as soon as possible, ie, as far down in the plan tree as possible. So we can treat the pathkeys as equivalent everywhere. The exception is when the relations A and B are joined inside the nullable side of an OUTER JOIN and the equijoin clause comes from above the OUTER JOIN. In this case we cannot apply the qual as soon as A and B are joined, so we do not consider the pathkeys to be equivalent. This could be improved if we wanted to go to the trouble of making pathkey equivalence be context-dependent, but that seems much more complex than it's worth.) In short, then: when producing the pathkeys for a merge or nestloop join, we can keep all of the keys of the outer path, since the ordering of the outer path will be preserved in the result. Furthermore, we can add to each pathkey sublist any inner vars that are equijoined to any of the outer vars in the sublist; this works regardless of whether we are implementing the join using that equijoin clause as a mergeclause, or merely enforcing the clause after-the-fact as a qpqual filter. Although Hashjoins also work only with equijoin operators, it is *not* safe to consider the output of a Hashjoin to be sorted in any particular order --- not even the outer path's order. This is true because the executor might have to split the join into multiple batches. Therefore a Hashjoin is always given NIL pathkeys. (Also, we need to use only mergejoinable operators when deducing which inner vars are now sorted, because a mergejoin operator tells us which left- and right-datatype sortops can be considered equivalent, whereas a hashjoin operator doesn't imply anything about sort order.) Pathkeys are also useful to represent an ordering that we wish to achieve, since they are easily compared to the pathkeys of a potential candidate path. So, SortClause lists are turned into pathkeys lists for use inside the optimizer. OK, now for how it *really* works: We did implement pathkeys just as described above, and found that the planner spent a huge amount of time comparing pathkeys, because the representation of pathkeys as unordered lists made it expensive to decide whether two were equal or not. So, we've modified the representation as described next. If we scan the WHERE clause for equijoin clauses (mergejoinable clauses) during planner startup, we can construct lists of equivalent pathkey items for the query. There could be more than two items per equivalence set; for example, WHERE A.X = B.Y AND B.Y = C.Z AND D.R = E.S creates the equivalence sets { A.X B.Y C.Z } and { D.R E.S } (plus associated sortops). Any pathkey item that belongs to an equivalence set implies that all the other items in its set apply to the relation too, or at least all the ones that are for fields present in the relation. (Some of the items in the set might be for as-yet-unjoined relations.) Furthermore, any multi-item pathkey sublist that appears at any stage of planning the query *must* be a subset of one or another of these equivalence sets; there's no way we'd have put two items in the same pathkey sublist unless they were equijoined in WHERE. Now suppose that we allow a pathkey sublist to contain pathkey items for vars that are not yet part of the pathkey's relation. This introduces no logical difficulty, because such items can easily be seen to be irrelevant; we just mandate that they be ignored. But having allowed this, we can declare (by fiat) that any multiple-item pathkey sublist must be "equal()" to the appropriate equivalence set. In effect, whenever we make a pathkey sublist that mentions any var appearing in an equivalence set, we instantly add all the other vars equivalenced to it, whether they appear yet in the pathkey's relation or not. And we also mandate that the pathkey sublist appear in the same order as the equivalence set it comes from. In fact, we can go even further, and say that the canonical representation of a pathkey sublist is a pointer directly to the relevant equivalence set, which is kept in a list of pathkey equivalence sets for the query. Then pathkey sublist comparison reduces to pointer-equality checking! To do this we also have to add single-element pathkey sublists to the query's list of equivalence sets, but that's a small price to pay. By the way, it's OK and even useful for us to build equivalence sets that mention multiple vars from the same relation. For example, if we have WHERE A.X = A.Y and we are scanning A using an index on X, we can legitimately conclude that the path is sorted by Y as well; and this could be handy if Y is the variable used in other join clauses or ORDER BY. So, any WHERE clause with a mergejoinable operator can contribute to an equivalence set, even if it's not a join clause. As sketched so far, equijoin operators allow us to conclude that A.X = B.Y and B.Y = C.Z together imply A.X = C.Z, even when different datatypes are involved. What is not immediately obvious is that to use the "canonical pathkey" representation, we *must* make this deduction. An example (from a real bug in Postgres 7.0) is a mergejoin for a query like SELECT * FROM t1, t2 WHERE t1.f2 = t2.f3 AND t1.f1 = t2.f3; The canonical-pathkey mechanism is able to deduce that t1.f1 = t1.f2 (ie, both appear in the same canonical pathkey set). If we sort t1 and then apply a mergejoin, we *must* filter the t1 tuples using the implied qualification f1 = f2, because otherwise the output of the sort will be ordered by f1 or f2 (whichever we sort on) but not both. The merge will then fail since (depending on which qual clause it applies first) it's expecting either ORDER BY f1,f2 or ORDER BY f2,f1, but the actual output of the sort has neither of these orderings. The best fix for this is to generate all the implied equality constraints for each equijoin set and add these clauses to the query's qualification list. In other words, we *explicitly* deduce f1 = f2 and add this to the WHERE clause. The constraint will be applied as a qpqual to the output of the scan on t1, resulting in sort output that is indeed ordered by both vars. This approach provides more information to the selectivity estimation code than it would otherwise have, and reduces the number of tuples processed in join stages, so it's a win to make these deductions even if we weren't forced to. When we generate implied equality constraints, we may find ourselves adding redundant clauses to specific relations. For example, consider SELECT * FROM t1, t2, t3 WHERE t1.a = t2.b AND t2.b = t3.c; We will generate the implied clause t1.a = t3.c and add it to the tree. This is good since it allows us to consider joining t1 and t3 directly, which we otherwise wouldn't do. But when we reach the stage of joining all three relations, we will have redundant join clauses --- eg, if we join t1 and t2 first, then the path that joins (t1 t2) to t3 will have both t2.b = t3.c and t1.a = t3.c as restriction clauses. This is bad; not only is evaluation of the extra clause useless work at runtime, but the selectivity estimator routines will underestimate the number of tuples produced since they won't know that the two clauses are perfectly redundant. We fix this by detecting and removing redundant clauses as the restriction clause list is built for each join. (We can't do it sooner, since which clauses are redundant will vary depending on the join order.) Yet another implication of all this is that mergejoinable operators must form closed equivalence sets. For example, if "int2 = int4" and "int4 = int8" are both marked mergejoinable, then there had better be a mergejoinable "int2 = int8" operator as well. Otherwise, when we're given WHERE int2var = int4var AND int4var = int8var, we'll fail while trying to create a representation of the implied clause int2var = int8var. An additional refinement we can make is to insist that canonical pathkey lists (sort orderings) do not mention the same pathkey set more than once. For example, a pathkey list ((A) (B) (A)) is redundant --- the second occurrence of (A) does not change the ordering, since the data must already be sorted by A. Although a user probably wouldn't write ORDER BY A,B,A directly, such redundancies are more probable once equijoin equivalences have been considered. Also, the system is likely to generate redundant pathkey lists when computing the sort ordering needed for a mergejoin. By eliminating the redundancy, we save time and improve planning, since the planner will more easily recognize equivalent orderings as being equivalent. Though Bob Devine <bob.devine@worldnet.att.net> was not involved in the coding of our optimizer, he is available to field questions about optimizer topics. -- bjm & tgl